On the use of Earth Observation to support estimates of national greenhouse gas emissions and sinks for the Global stocktake process: lessons learned from ESA-CCI RECCAP2

The Global Stocktake (GST), implemented by the Paris Agreement, requires rapid developments in the capabilities to quantify annual greenhouse gas (GHG) emissions and removals consistently from the global to the national scale and improvements to national GHG inventories. In particular, new capabilities are needed for accurate attribution of sources and sinks and their trends to natural and anthropogenic processes. On the one hand, this is still a major challenge as national GHG inventories follow globally harmonized methodologies based on the guidelines established by the Intergovernmental Panel on Climate Change, but these can be implemented differently for individual countries. Moreover, in many countries the capability to systematically produce detailed and annually updated GHG inventories is still lacking. On the other hand, spatially-explicit datasets quantifying sources and sinks of carbon dioxide, methane and nitrous oxide emissions from Earth Observations (EO) are still limited by many sources of uncertainty. While national GHG inventories follow diverse methodologies depending on the availability of activity data in the different countries, the proposed comparison with EO-based estimates can help improve our understanding of the comparability of the estimates published by the different countries. Indeed, EO networks and satellite platforms have seen a massive expansion in the past decade, now covering a wide range of essential climate variables and offering high potential to improve the quantification of global and regional GHG budgets and advance process understanding. Yet, there is no EO data that quantifies greenhouse gas fluxes directly, rather there are observations of variables or proxies that can be transformed into fluxes using models. Here, we report results and lessons from the ESA-CCI RECCAP2 project, whose goal was to engage with National Inventory Agencies to improve understanding about the methods used by each community to estimate sources and sinks of GHGs and to evaluate the potential for satellite and in-situ EO to improve national GHG estimates. Based on this dialogue and recent studies, we discuss the potential of EO approaches to provide estimates of GHG budgets that can be compared with those of national GHG inventories. We outline a roadmap for implementation of an EO carbon-monitoring program that can contribute to the Paris Agreement. Supplementary Information The online version contains supplementary material available at 10.1186/s13021-022-00214-w.

We then ran four additional simulations, corresponding to S2 and S3 in the TRENDY project (Sitch et al., 2015), forced with either LUH2 (SX.1) or HILDA+ (SX.2) land-use. In the S2.X simulations, models were forced with transient climate  and fixed land-use map of 1950 from LUH2 (S2.1) and from HILDA+ (S2.2). In the S2.X simulations, models were forced with transient climate  and land-use change maps of 1950-2020 from LUH2 (S3.1) and 1950-2015 from HILDA+ (S3.2). It should be noted that all models are forced with net LULCC, and not gross transitions and JULES did not consider harvest in these simulations. In the second priority simulations, the models were initialised at the end of the first priority runs and extended until mid-2021 (31 st of July). Here we show the results of the complete runs, i.e. 1950 -June 2021.
Additionally, we use the results of the simulations by the bookkeeping model BLUE forced with HILDA+ by (Ganzenmüller et al., 2022). These followed the standard GCB protocol but used HILDA+ as input for LULCC transitions.

National GHG Inventory data
All UNFCCC parties should periodically submit their national greenhouse gas inventories (NGHGIs) to the UNFCCC secretariat. For Annex I parties, they are required to submit NGHGIs annually covering emissions and removals of main greenhouse gases from five categories (energy; industrial processes and product use; agriculture; land use, land-use change and forestry (LULUCF); and waste) and its subsectors, and for all years since 1990. The LUC data in this study are extracted from the annual NGHGIs reported in the Common Report Format (CRF) tables submitted by the Annex I parties, which are available at https://unfccc.int/ghg-inventories-annex-i-parties/2021.

Wetland CH4 emissions
Spatio-temporal wetland emission fluxes of CH4 derived from in-situ and satellite-based inversions in the Global Methane Budget (Saunois et al., 2020). The datasets cover at least 2010-2017. Methane inversions have been processed by three different methods to remove natural fluxes and separate anthropogenic emissions that can be compared with inventories. For a full description of the inversion datasets we refer to the original publication (Saunois et al., 2020).
Here we compare two groups of simulations of wetland CH4 emissions simulated by the DGVMs in the latest Global Methane Budget (Saunois et al., 2020), a group of simulations with prescribed wetland extent (DGVMDIAG) and another where models simulate wetland extent prognostically (DGVMPROG). The models were forced with climate data from the CRU-JRA reanalysis (Harris, 2019). The wetland distribution in the DGVMDIAG runs is based on the Wetland Area and Dynamics for Methane Modeling, WAD2M, (Zhang et al., 2021)). The group of models participating in each simulation is indicated in Table S2. All datasets were remapped to a common 1×1degree grid with consistent land ocean mask.

Table S2
Models simulating wetland CH4 emissions and corresponding simulations performed in the Global Methane Budget. For full references, model description and simulation protocol, see Saunois et al. (2020).

Model
Diagnostic Prognostic

Land Use Harmonization
The Land-Use Harmonization (LUH2) project developed and set of harmonized historical reconstructions of land-use connected with the future projections to be used in CMIP6 (Hurtt et al., 2020). LUH2 provides cropland, pasture, urban and ice/water fractions since 850 based on HYDE (Klein Goldewijk et al., 2011), which uses country-level agricultural areas (cropland, pasture, rangelands) data after 1961 from FAO, extrapolated backwards in time using total population and agricultural area per-capita ratios for each country, and ESA-CCI land-cover to distribute national totals in space. These two datasets have been regularly updated in the successive rounds of Global Carbon Budgets, where estimates of recent years are extrapolated given the lag with FAO reports and detected errors are corrected . Here we use the LUH2 version of GCB2021 covering the entire 850-2020 period. This updated version uses the most recent HYDE/FAO release, and multi-annual ESA-CCI land cover maps (1992-2018) for spatial disaggregation (replacing the single ESA land cover reference year in HYDE3.2 and earlier versions) which introduced large differences compared to previous versions (Friedlingstein et al., 2021).

HILDA+
The HILDA + (Historic Land Dynamics Assessment +) dataset (Winkler et al., 2021) combines multiple high-resolution remote sensing data with long-term land use and population statistics from FAO to assess annual states and respective changes in LULCC from 1960 to 2019 at a spatial resolution of 1 km. The remote sensing datasets include diverse land-cover datasets with global (e.g. ESA-CCI, Copernicus LC100, GlobCover, GLAD UMD Vegetation Cover Fraction, GCL2000, MODIS) and regional coverage (e.g. CORINE, NLCD Land Cover), combined with data on human settlements and urban and grazing areas. From these datasets, HILDA+ derived annual gross changes between six land-cover categories: urban, cropland, pasture/rangeland, forest, unmanaged grass/shrubland, sparse/no vegetation. In addition to land-use classification and changes, HILDA+ provides annual quantification of uncertainties in these variables, based on the agreement/disagreement between the multiple datasets used.
Here, we used a preliminary version of HILDA+ provided by K. Winkler and R. Fuchs (v0.2), which covered the period 1950-2015. Compared to HILDA+ version 1.0, the preliminary version used here (v0.2) did not use the Corine Land Cover maps for 2012 and 2018, nor the high resolution Copernicus LC100, did not consider the forest dynamics/sub-division included from ESA-CCI, did not include the 2020 update of FAO data and used a minimum LULC fraction threshold for change allocation procedure of 0.0 (methods fully described in (Winkler et al., 2021)). We expect these changes to not affect the results presented here significantly, since they have a potential limited effect in Europe. Based on the 1km maps of land-use states, we derived land-use fractions at 0.25×0.25degree spatial resolution for the 13 land-cover classes common to OCN and ORCHIDE-MICT following the relationships shown in Table S3. Since HILDA+ does not distinguish between climatic zones, the forest classes from HILDA+ were grouped into boreal, temperate and tropical following the distribution of the climate zones in OCN. Similarly, OCN and ORCHIDE-MICT distinguish between C3 and C4 grasses and crops. To assign HILDA+ grasslands/croplands to C3/C4, we used the map from (Still et al., 2009). Inevitably, this conversion carries uncertainties. For example, HILDA+ class 55, grass/shrublands includes prairies, steppes, savannahs, mosaics with trees and shrubs as well as herbaceous wetlands, some of which might be better represented in the models as a forest PFT. A similar conversion has been made to the BLUE PFTs and used to force the BLUE BK model. This is described in detail in (Ganzenmüller et al., 2022) .  (Still et al., 2009)